MXNet Notebooks

This repo contains various notebooks ranging from basic usages of MXNet to state-of-the-art deep learning applications.

How to use

Python

The python notebooks are written in Jupyter.

• View We can view the notebooks on either github or nbviewer. But note that the former may be failed to render a page, while the latter has delays to view the recent changes.

• Run We can run and modify these notebooks if both mxnet and jupyter are installed. Here is an example script to install all these packages on Ubuntu.

  If you have a AWS account, here is an easier way to run the notebooks:

  1. Launch a p2.xlarge instance by using AMI ami-6e5d6808 on Ireland (eu-west-1). The Deep Learning AMI v2.0 for Amazon Linux is designed to continue to provide a stable, secure, and high performance execution environment for deep learning applications running on Amazon EC2.

Linux and OSX Users:

2. Once launch has succeeded we'll setup a few variables with the proper hostnames, and then connect via SSH. At the same time we'll enable port 8888 to be tunneled over our connection so that we can eventually access our jupyter notebooks with a local browser. Note when copying from the console it may prepend your hostname with root. For the AMI we are using the correct user is ec2-user.

     export HOSTNAME=ec2-107-22-159-132.compute-1.amazonaws.com
     export PERM=~/Downloads/my.pem
     chmod 400 $PERM
     ssh -i $PERM -L 8888:localhost:8888 ec2-user@$HOSTNAME

Windows Users:

2. Unfortunately getting connected with Windows requires a few additional steps. You'll need to install Putty and then follow two sections of this guide. First follow the 'Converting Your Private Key Using PuTTYgen' and then follow the 'Starting a PuTTY Session' section. Important: Before opening a connection there's one last setting to set which will allow you to connect locally to your jupyter notebook. Open the setting under Connection->SSH->Tunnels and add a tunnel from source port 8888 to local port 8888. Now you can proceed to connect and get a shell the the EC2 instance.

3. Once you've connected to your EC2 machine, clone this repo on the EC2 machine and run jupyter

     sudo yum install -y graphviz
     sudo mkdir /efs
     sudo chown ec2-user:ec2-user /efs
     cd /efs
     git clone https://github.com/dmlc/mxnet-notebooks
     jupyter notebook

   Leave this ssh session open and connected while using the python notebooks.

4. Now we are able to view and edit the notebooks on the browser using the URL: http://localhost:8888/tree/mxnet-notebooks/python/outline.ipynb

5. Finally you may want to connect another ssh session and run the following command to keep track of GPU memory and core usage

   ssh -i $PERM ec2-user@$HOSTNAME
   watch -n 1 nvidia-smi

How to develop

Some general guidelines

• A notebook covers a single concept or application
• Try to be as basic as possible. Put advanced usages at the end, and allow reader to skip it.
• Keep the cell outputs on the notebooks so that readers can see the results without running